ICLR 2025 Workshop on Bidirectional Human-AI Alignment

Hua Shen, Ziqiao Ma, Reshmi Ghosh, Tiffany Knearem, Michael Xieyang Liu, Tongshuang Wu, Andrés Monroy-Hernández, Diyi Yang, Antoine Bosselut, Furong Huang, Tanu Mitra, Joyce Chai, Marti Hearst, Dawn Song, Yang Li

Keywords: Human-AI Alignment, Bidirectional Alignment, Human-Centered AI

TL;DR: A workshop proposal for bidirectional human-AI alignment that aims to improve AI from the the dynamic, and mutual alignment processing perspective

Abstract: As AI systems grow more integrated into real-world applications, the traditional one-way approach to AI alignment is proving insufficient. Bidirectional Human-AI Alignment proposes a new, dynamic framework where alignment is viewed as an ongoing, reciprocal process, with both humans and AI systems adapting over time. This paradigm acknowledges the complexity of human-AI interactions and emphasizes the need for continuous adaptation to evolving human values, societal contexts, and feedback loops. Our workshop at ICLR 2025 focuses on machine learning techniques that can drive this bidirectional alignment, including reinforcement learning, interactive learning, and multi-task learning, enabling AI systems to evolve in response to real-world changes. We also explore value specification, human-in-the-loop frameworks, and scalable post-training alignment methods. Additionally, the workshop will address evaluation techniques for real-time alignment adjustments and the societal implications of maintaining alignment across diverse human populations. By fostering collaboration between AI, HCI, and social science researchers, the workshop aims to create scalable, adaptive alignment frameworks that reflect ethical and societal goals. This event offers a novel approach to alignment research, emphasizing mutual human-AI adaptation and interdisciplinary cooperation to ensure AI systems remain aligned with human values.

Submission Number: 12